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Deploying unmanned aerial vehicle (UAV) swarms in delivery systems are still in its infancy with regard to the technology, safety, and aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects, e.g., increasing efficacy and reducing the air traffic, resulting in a safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This paper analyzes several delivery schemes within a platform, such as delivery with and without using air highways and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium, and first-in first-out) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space,” and several other new techniques along with Hungarian and cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method, and constraints. Several scenarios are tested on the simulator using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and the method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient, and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs. Additionally, private industries, logistics operators, and municipalities are expected to benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical implementation of UAV delivery systems.